draft

load librraies

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Exploring built in datasets

data()

BOD 

##   Time demand
## 1    1    8.3
## 2    2   10.3
## 3    3   19.0
## 4    4   16.0
## 5    5   15.6
## 6    7   19.8

Exploring datasets from packages

library(gapminder)

gapminder

## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows

Exploring dataset from web

ca <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/ca.csv") 

## Rows: 789 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(ca)

## Rows: 789
## Columns: 7
## $ region    <chr> "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", "PW", …
## $ state     <chr> "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", "CA", …
## $ code      <chr> "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHIS", "CHI…
## $ park_name <chr> "Channel Islands National Park", "Channel Islands National P…
## $ type      <chr> "National Park", "National Park", "National Park", "National…
## $ visitors  <dbl> 1200, 1500, 1600, 300, 15700, 31000, 33100, 32000, 24400, 31…
## $ year      <dbl> 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, …

First plot

ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
  geom_point()

## More arguments in geoms

ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
  geom_point(alpha= 0.5) +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## customisation theme

ggplot(ca, aes(x=year, y = visitors, color = park_name)) +
  geom_point(alpha= 0.5) +
  geom_smooth() + 
  labs(x = "Year",
       y = "Visitation",
       title = "California National Park Visitation") +
  theme_bw() +
  theme(
    legend.title = element_blank(), 
    axis.title.x = element_text(angle=45, size =20))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = ca, aes(x = year, y = visitors, color = code)) +
    geom_point() +
  facet_wrap(~ code, scales = "free") +
  theme_bw()

Gapminder Data

library(gapminder)

gapminder %>% 
  ggplot(aes(x = log(gdpPercap), y = lifeExp, col = year, size = pop)) +
  geom_point(alpha = 0.3) +
  geom_smooth(method = lm) +
  facet_wrap( ~ continent, scales = "free") +
  theme_bw()

## `geom_smooth()` using formula 'y ~ x'

gapminder %>% 
  ggplot(aes( x= continent, y = lifeExp, col = continent)) +
  geom_boxplot() +
  geom_jitter(alpha = 0.3) +
  labs(
    x = "Life Expectancy",
    y = "Continent", 
    colour = "Continent"
  )

  theme_bw() 

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Southeast US National Parks

# Southeast US National Parks
se <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/se.csv")

## Rows: 453 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

geom is the geometrical object that a plot uses to represent data. People often describe plots by the type of geom that the plot uses. For example, bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on. Scatterplots break the trend; they use the point geom. You can use different geoms to plot the same data. To change the geom in your plot, change the geom function that you add to ggplot(). Let’s look at a few ways of viewing the distribution of annual visitation (visitors) for each park (park_name).

# flip
# add colour
# take out the legend

ggplot(data = se) + 
  geom_jitter(aes(x = park_name, y = visitors, color = park_name, alpha = 0.5)) +
  coord_flip() +
  theme_bw() +
  theme(
    legend.position = "none"
  )

visit_16 <- read_csv("https://raw.githubusercontent.com/ScienceParkStudyGroup/r-lesson-based-on-ohi-data-training/gh-pages/data/visit_16.csv")

## Rows: 17 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): region, state, code, park_name, type
## dbl (2): visitors, year
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

x <- ggplot(data = visit_16, aes(x = state, y = visitors, fill = park_name)) + 
  geom_bar(stat = "identity", position = "dodge") +
  theme_bw()

x

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

ggplotly(x)

plot <- ggplot(data = se) + 
  geom_jitter(aes(x = park_name, y = visitors, color = park_name, alpha = 0.5)) +
  coord_flip() +
  theme_bw() +
  theme(
    legend.position = "none"
  )

ggplotly(plot)

Save the plot

After creating your plot, you can save it to a file in your favorite format. The Export tab in the Plot pane in RStudio will save your plots at low resolution, which will not be accepted by many journals and will not scale well for posters.

Instead, use the ggsave() function, which allows you easily change the dimension and resolution of your plot by adjusting the appropriate arguments (width, height and dpi):

#ggsave(here("results","plot.png"), x, width = 15, height = 10)